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pro vyhledávání: '"Memeti A"'
Autor:
Memeti, Suejb
Heterogeneous systems, consisting of CPUs and GPUs, offer the capability to address the demands of compute- and data-intensive applications. However, programming such systems is challenging, requiring knowledge of various parallel programming framewo
Externí odkaz:
http://arxiv.org/abs/2311.03543
The utilization of performance monitoring probes is a valuable tool for programmers to gather performance data. However, the manual insertion of these probes can result in an increase in code size, code obfuscation, and an added burden of learning di
Externí odkaz:
http://arxiv.org/abs/2311.03535
Autor:
Reka Fatime Hasani, Memeti Memet
Publikováno v:
SEEU Review, Vol 19, Iss 1, Pp 116-131 (2024)
The enduring global issue of women's underrepresentation in science, technology, engineering, and mathematics (STEM) fields carries significant implications for both societal advancement and economic growth. Despite the rise in women's enrollment in
Externí odkaz:
https://doaj.org/article/b8a2162f8b1348fd92f97a134130073d
Any data analysis, especially the data sets that may be changing often or in real-time, consists of at least three important synchronized components: i) figuring out what to infer (objectives), ii) analysis or computation of objectives, and iii) unde
Externí odkaz:
http://arxiv.org/abs/2106.05357
Autor:
Memeti, Suejb, Pllana, Sabri
Heterogeneous computing systems provide high performance and energy efficiency. However, to optimally utilize such systems, solutions that distribute the work across host CPUs and accelerating devices are needed. In this paper, we present a performan
Externí odkaz:
http://arxiv.org/abs/2106.01441
Determining the optimal location of control cabinet components requires the exploration of a large configuration space. For real-world control cabinets it is impractical to evaluate all possible cabinet configurations. Therefore, we need to apply met
Externí odkaz:
http://arxiv.org/abs/1906.04825
Many complex problems, such as natural language processing or visual object detection, are solved using deep learning. However, efficient training of complex deep convolutional neural networks for large data sets is computationally demanding and requ
Externí odkaz:
http://arxiv.org/abs/1906.01992
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Autor:
Memeti, Suejb, Pllana, Sabri
Big data streaming applications require utilization of heterogeneous parallel computing systems, which may comprise multiple multi-core CPUs and many-core accelerating devices such as NVIDIA GPUs and Intel Xeon Phis. Programming such systems require
Externí odkaz:
http://arxiv.org/abs/1809.09387